Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x104908c18>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x105245a58>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
/Users/Tote/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    real_input_images = tf.placeholder(dtype=tf.float32, shape=[None, image_width, image_height, image_channels], name='real_input')
    z_data = tf.placeholder(dtype=tf.float32, shape=[None, z_dim], name='z')
    learning_rate = tf.placeholder(dtype=tf.float32, shape=[], name='learning_rate')

    return real_input_images, z_data, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [6]:
def discriminator(images, reuse=False, alpha= 0.2):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    #28x28x3
    with tf.variable_scope("discriminator", reuse=reuse):
        x1 = tf.layers.conv2d(inputs=images, filters=64, kernel_size=(5, 5), strides=2, padding="valid")
        relu1 = tf.maximum(x1 * alpha, x1)
        #12x12x64
        
        x2 = tf.layers.conv2d(relu1, 128, (5, 5), 2, padding="same")
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(bn2 * alpha, bn2)
        #6x6x128
        
        x3 = tf.layers.conv2d(relu2, 256, (3, 3), 1, padding="same")
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(bn3 * alpha, bn3)
        #6x6x256
        
        #flatten image
        flat = tf.reshape(relu3, [-1, 6*6*256])
        
        logits = tf.layers.dense(inputs=flat, units=1)
        out = tf.sigmoid(logits)

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2, reuse=False):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope("generator", reuse=not is_train):
        x1 = tf.layers.dense(inputs=z, units=7*7*512)
        x1 = tf.reshape(x1, [-1, 7, 7, 512])
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(x1 * alpha, x1)
        # 7x7x512
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding="same")
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(x2 * alpha, x2)
        #14x14x256
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding="same")
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(x3 * alpha, x3)
        #28x28x128
        
        x4 = tf.layers.conv2d_transpose(x3, 64, 3, strides=1, padding="same")
        x4 = tf.layers.batch_normalization(x4, training=is_train)
        x4 = tf.maximum(x4*alpha, x4)
        #28x28x64
        
        logits = tf.layers.conv2d(x4, out_channel_dim, 5, strides=1, padding="same")
        #28x28xout_channel_dim
        out = tf.tanh(logits)
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim, alpha=0.2):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    
    g_model = generator(input_z, out_channel_dim, alpha=alpha)
    d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Retrieve weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    #optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
        
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    _, image_width, image_height, num_channels = data_shape
    real_input, z, learning_rate_t = model_inputs(image_width, image_height, num_channels, z_dim)
    d_loss_t, g_loss_t = model_loss(real_input, z, num_channels)
    d_train_opt, g_train_opt = model_opt(d_loss_t, g_loss_t, learning_rate, beta1)
    
    g_losses = []
    d_losses = []
    steps = 0
    print_step = 20
    show_step = 100
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                feed = {real_input: batch_images, z: batch_z, learning_rate_t: learning_rate}
                
                _, _ = sess.run([g_loss_t, g_train_opt], feed_dict=feed)
                g_loss, _ = sess.run([g_loss_t, g_train_opt], feed_dict=feed)
                d_loss, _ = sess.run([d_loss_t, d_train_opt], feed_dict=feed)
                d_losses.append(d_loss)
                g_losses.append(g_loss)
                
                if steps % print_step == 0:
                    
                    print("Step {}, Epoch {}/{}...".format(steps, epoch_i+1, epoch_count),
                          "Discriminator loss: {:.4f}...".format(d_loss),
                          "Generator loss: {:.4f}".format(g_loss))
                    
                #if steps % show_step == 0:
                    show_generator_output(sess, 32, z, num_channels, data_image_mode)
                steps += 1

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 64
z_dim = 120
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Step 0, Epoch 1/2... Discriminator loss: 6.0069... Generator loss: 0.0230
Step 20, Epoch 1/2... Discriminator loss: 3.3653... Generator loss: 0.7205
Step 40, Epoch 1/2... Discriminator loss: 2.4968... Generator loss: 0.5930
Step 60, Epoch 1/2... Discriminator loss: 1.8873... Generator loss: 0.5821
Step 80, Epoch 1/2... Discriminator loss: 2.0329... Generator loss: 0.6730
Step 100, Epoch 1/2... Discriminator loss: 1.6620... Generator loss: 0.6733
Step 120, Epoch 1/2... Discriminator loss: 1.4486... Generator loss: 0.9176
Step 140, Epoch 1/2... Discriminator loss: 1.2734... Generator loss: 1.0901
Step 160, Epoch 1/2... Discriminator loss: 1.4504... Generator loss: 0.8499
Step 180, Epoch 1/2... Discriminator loss: 1.2467... Generator loss: 0.9674
Step 200, Epoch 1/2... Discriminator loss: 1.1520... Generator loss: 1.1168
Step 220, Epoch 1/2... Discriminator loss: 1.3023... Generator loss: 1.2752
Step 240, Epoch 1/2... Discriminator loss: 1.2161... Generator loss: 1.1748
Step 260, Epoch 1/2... Discriminator loss: 0.8745... Generator loss: 1.5121
Step 280, Epoch 1/2... Discriminator loss: 1.0432... Generator loss: 0.9428
Step 300, Epoch 1/2... Discriminator loss: 0.9508... Generator loss: 1.2641
Step 320, Epoch 1/2... Discriminator loss: 0.8528... Generator loss: 1.6606
Step 340, Epoch 1/2... Discriminator loss: 1.0771... Generator loss: 1.0724
Step 360, Epoch 1/2... Discriminator loss: 1.0507... Generator loss: 1.2229
Step 380, Epoch 1/2... Discriminator loss: 0.7127... Generator loss: 1.7220
Step 400, Epoch 1/2... Discriminator loss: 1.1383... Generator loss: 1.5795
Step 420, Epoch 1/2... Discriminator loss: 1.0597... Generator loss: 2.1413
Step 440, Epoch 1/2... Discriminator loss: 1.1759... Generator loss: 1.1588
Step 460, Epoch 1/2... Discriminator loss: 1.1286... Generator loss: 1.1680
Step 480, Epoch 1/2... Discriminator loss: 0.9763... Generator loss: 1.7712
Step 500, Epoch 1/2... Discriminator loss: 1.1959... Generator loss: 0.9052
Step 520, Epoch 1/2... Discriminator loss: 1.0867... Generator loss: 1.7034
Step 540, Epoch 1/2... Discriminator loss: 1.2717... Generator loss: 1.6196
Step 560, Epoch 1/2... Discriminator loss: 1.1442... Generator loss: 0.7191
Step 580, Epoch 1/2... Discriminator loss: 1.2162... Generator loss: 0.9362
Step 600, Epoch 1/2... Discriminator loss: 1.0874... Generator loss: 1.8878
Step 620, Epoch 1/2... Discriminator loss: 1.0339... Generator loss: 1.4075
Step 640, Epoch 1/2... Discriminator loss: 0.9253... Generator loss: 1.1264
Step 660, Epoch 1/2... Discriminator loss: 1.1320... Generator loss: 1.5423
Step 680, Epoch 1/2... Discriminator loss: 0.9436... Generator loss: 1.3015
Step 700, Epoch 1/2... Discriminator loss: 1.0026... Generator loss: 1.5237
Step 720, Epoch 1/2... Discriminator loss: 0.9890... Generator loss: 1.3225
Step 740, Epoch 1/2... Discriminator loss: 0.8818... Generator loss: 1.6555
Step 760, Epoch 1/2... Discriminator loss: 0.6869... Generator loss: 1.7098
Step 780, Epoch 1/2... Discriminator loss: 1.2601... Generator loss: 0.6643
Step 800, Epoch 1/2... Discriminator loss: 0.9019... Generator loss: 1.7749
Step 820, Epoch 1/2... Discriminator loss: 1.1481... Generator loss: 0.7694
Step 840, Epoch 1/2... Discriminator loss: 0.9447... Generator loss: 1.8325
Step 860, Epoch 1/2... Discriminator loss: 1.3523... Generator loss: 0.5741
Step 880, Epoch 1/2... Discriminator loss: 0.8537... Generator loss: 1.6934
Step 900, Epoch 1/2... Discriminator loss: 1.0252... Generator loss: 1.0325
Step 920, Epoch 1/2... Discriminator loss: 1.0506... Generator loss: 0.7754
Step 940, Epoch 2/2... Discriminator loss: 0.9917... Generator loss: 1.3985
Step 960, Epoch 2/2... Discriminator loss: 0.7257... Generator loss: 1.8647
Step 980, Epoch 2/2... Discriminator loss: 1.1305... Generator loss: 1.9244
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-12-654366ecdf24> in <module>()
     13 with tf.Graph().as_default():
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
---> 15           mnist_dataset.shape, mnist_dataset.image_mode)

<ipython-input-11-0eb7808ffd08> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)
     34 
     35                 _, _ = sess.run([g_loss_t, g_train_opt], feed_dict=feed)
---> 36                 g_loss, _ = sess.run([g_loss_t, g_train_opt], feed_dict=feed)
     37                 d_loss, _ = sess.run([d_loss_t, d_train_opt], feed_dict=feed)
     38                 d_losses.append(d_loss)

/Users/Tote/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    765     try:
    766       result = self._run(None, fetches, feed_dict, options_ptr,
--> 767                          run_metadata_ptr)
    768       if run_metadata:
    769         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/Users/Tote/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    963     if final_fetches or final_targets:
    964       results = self._do_run(handle, final_targets, final_fetches,
--> 965                              feed_dict_string, options, run_metadata)
    966     else:
    967       results = []

/Users/Tote/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1013     if handle is None:
   1014       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1015                            target_list, options, run_metadata)
   1016     else:
   1017       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/Users/Tote/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1020   def _do_call(self, fn, *args):
   1021     try:
-> 1022       return fn(*args)
   1023     except errors.OpError as e:
   1024       message = compat.as_text(e.message)

/Users/Tote/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1002         return tf_session.TF_Run(session, options,
   1003                                  feed_dict, fetch_list, target_list,
-> 1004                                  status, run_metadata)
   1005 
   1006     def _prun_fn(session, handle, feed_dict, fetch_list):

KeyboardInterrupt: 

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 64
z_dim = 120
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Step 0, Epoch 1/1... Discriminator loss: 9.0891... Generator loss: 0.0016
Step 20, Epoch 1/1... Discriminator loss: 3.2409... Generator loss: 1.3850
Step 40, Epoch 1/1... Discriminator loss: 2.3251... Generator loss: 0.8733
Step 60, Epoch 1/1... Discriminator loss: 1.5794... Generator loss: 1.2508
Step 80, Epoch 1/1... Discriminator loss: 1.8125... Generator loss: 1.5196
Step 100, Epoch 1/1... Discriminator loss: 0.8189... Generator loss: 1.9314
Step 120, Epoch 1/1... Discriminator loss: 0.5456... Generator loss: 3.4047
Step 140, Epoch 1/1... Discriminator loss: 2.0249... Generator loss: 1.3135
Step 160, Epoch 1/1... Discriminator loss: 2.3068... Generator loss: 0.6760
Step 180, Epoch 1/1... Discriminator loss: 1.8546... Generator loss: 0.6716
Step 200, Epoch 1/1... Discriminator loss: 1.5719... Generator loss: 0.6969
Step 220, Epoch 1/1... Discriminator loss: 1.5709... Generator loss: 0.7100
Step 240, Epoch 1/1... Discriminator loss: 1.6085... Generator loss: 0.6929
Step 260, Epoch 1/1... Discriminator loss: 1.6059... Generator loss: 0.6207
Step 280, Epoch 1/1... Discriminator loss: 1.4897... Generator loss: 0.7000
Step 300, Epoch 1/1... Discriminator loss: 1.5378... Generator loss: 0.7152
Step 320, Epoch 1/1... Discriminator loss: 1.5358... Generator loss: 0.6986
Step 340, Epoch 1/1... Discriminator loss: 1.5944... Generator loss: 0.6806
Step 360, Epoch 1/1... Discriminator loss: 1.5176... Generator loss: 0.6430
Step 380, Epoch 1/1... Discriminator loss: 1.4714... Generator loss: 0.7310
Step 400, Epoch 1/1... Discriminator loss: 1.5045... Generator loss: 0.6652
Step 420, Epoch 1/1... Discriminator loss: 1.4902... Generator loss: 0.7032
Step 440, Epoch 1/1... Discriminator loss: 1.4875... Generator loss: 0.6988

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.